MICCAI 2022 Daily – Wednesday

“ We reduce the amount of shared information to just a mean and standard deviation per feature per site, ” she reveals. “ What this means is that rather than sharing the whole amount of data, or an entire feature embedding, you’re sharing about 96 pieces of information. ” Encoding the information while ensuring privacy was challenging because standard approaches do not protect privacy. However, by encoding it as the mean and standard deviation, this method could share just that information and then create example features by pulling them from a Gaussian distribution. “ We’re modeling features as a Gaussian distribution, ” Nicola explains. “ It’s a deep learning-based approach. We use an iterative framework that allows us to remove the information adversarially. We do the task we’re interested in while removing the scanner information, but we use the Gaussian distribution to generate features for the sites that we’re not currently training at to protect the privacy of the individuals. ” Could this novel element be what led to the paper being accepted at MICCAI this year? “ Yeah, I think it goes substantially beyond the existing approaches because they share the whole feature embedding, ” she responds. “ From that feature embedding, you would be able to reconstruct the image, 15 DAILY MICCAI Wednesday Nicola Dinsdale